@article{Bechtold-2020-Improved,
title = "Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework",
author = {Bechtold, Michel and
Lannoy, Gabri{\"e}lle De and
Reichle, Rolf H. and
Roose, Dirk and
Balliston, Nicole and
Burdun, Iuliia and
Devito, K. J. and
Kurbatova, J. and
Strack, Maria and
Zarov, Evgeny A.},
journal = "Remote Sensing of Environment, Volume 246",
volume = "246",
year = "2020",
publisher = "Elsevier BV",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G20-47001",
doi = "10.1016/j.rse.2020.111805",
pages = "111805",
abstract = "Abstract There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.",
}
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<abstract>Abstract There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.</abstract>
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%0 Journal Article
%T Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework
%A Bechtold, Michel
%A Lannoy, Gabriëlle De
%A Reichle, Rolf H.
%A Roose, Dirk
%A Balliston, Nicole
%A Burdun, Iuliia
%A Devito, K. J.
%A Kurbatova, J.
%A Strack, Maria
%A Zarov, Evgeny A.
%J Remote Sensing of Environment, Volume 246
%D 2020
%V 246
%I Elsevier BV
%F Bechtold-2020-Improved
%X Abstract There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.
%R 10.1016/j.rse.2020.111805
%U https://gwf-uwaterloo.github.io/gwf-publications/G20-47001
%U https://doi.org/10.1016/j.rse.2020.111805
%P 111805
Markdown (Informal)
[Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework](https://gwf-uwaterloo.github.io/gwf-publications/G20-47001) (Bechtold et al., GWF 2020)
ACL
- Michel Bechtold, Gabriëlle De Lannoy, Rolf H. Reichle, Dirk Roose, Nicole Balliston, Iuliia Burdun, K. J. Devito, J. Kurbatova, Maria Strack, and Evgeny A. Zarov. 2020. Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework. Remote Sensing of Environment, Volume 246, 246:111805.
